scholarly journals Clustering Nominal data with Equivalent Categories

2008 ◽  
Vol 35 (1) ◽  
pp. 35-54 ◽  
Author(s):  
Marian Hickendorff ◽  
Willem J. Heiser ◽  
Cornells M. van Putten ◽  
Norman D. Verhelst
Keyword(s):  
Nominal Data ◽  
Equivalent Categories

Sebaran Peluang Acak Kontinu, Distribusi Normal, Distribusi Normal Baku, Distribusi T, Distribusi Chi Square, dan Distribusi F

2020 ◽  
Author(s):  
Ahmad Sudi Pratikno
Keyword(s):  
Hypothesis Test ◽  
Standard Normal Distribution ◽  
Process Data ◽  
Nominal Data ◽  
Chi Square ◽  
Stable Curve ◽  
Standard Normal ◽  
Research Findings ◽  
F Distribution ◽  
T Distribution

In statistics, there are various terms that may feel unfamiliar to researcher who is not accustomed to discussing it. However, despite all of many functions and benefits that we can get as researchers to process data, it will later be interpreted into a conclusion. And then researcher can digest and understand the research findings. The distribution of continuous random opportunities illustrates obtaining opportunities with some detection of time, weather, and other data obtained from the field. The standard normal distribution represents a stable curve with zero mean and standard deviation 1, while the t distribution is used as a statistical test in the hypothesis test. Chi square deals with the comparative test on two variables with a nominal data scale, while the f distribution is often used in the ANOVA test and regression analysis.

Measuring interrater reliability among multiple raters: An example of methods for nominal data

1990 ◽  
Vol 9 (9) ◽  
pp. 1103-1115 ◽  
Author(s):  
Karen L. Posner ◽  
Paul D. Sampson ◽  
Robert A. Caplan ◽  
Richard J. Ward ◽  
Frederick W. Cheney
Keyword(s):  
Interrater Reliability ◽  
Nominal Data

scholarly journals Impact of Screening for Methicillin-Resistant Staphylococcus aureus (MRSA) in Pneumonia on Vancomycin Utilization

2020 ◽  
Vol 41 (S1) ◽  
pp. s265-s265
Author(s):  
Matthew Crotty ◽  
Natalie Weltman ◽  
Joslyn Pribble ◽  
Marie Wilson
Keyword(s):  
Staphylococcus Aureus ◽  
Clinical Outcomes ◽  
Nominal Data ◽  
Specific Patient ◽  
Methicillin Resistant ◽  
Process Implementation ◽  
Group Data ◽  
Days Of Therapy ◽  
High Negative Predictive Value ◽  
Utilization Data

Background: Methicillin-Resistant Staphylococcus aureus (MRSA) is frequently targeted with empiric treatment for pneumonia in the hospital. Obtaining quality lower respiratory tract cultures to promote appropriate de-escalation can be difficult or impractical. Nasal screening for MRSA has a high negative predictive value for MRSA pneumonia and can be an effective tool for early de-escalation. Methods: A pharmacist-driven process for nasopharyngeal MRSA screening of patients prescribed intravenous vancomycin was implemented in October 2018. Vancomycin utilization was extracted from the electronic medical record (EMR) and summarized as days of therapy per 1,000 patient days (DOT/1,000 PD). Vancomycin utilization data for the 6 months following process implementation (November 2018–April 2019) were compared to the same period from the previous year (November 2017–April 2018). Specific patient outcomes data were manually collected for patients prescribed vancomycin for pneumonia during the first 2 months following process implementation (November–December 2018; postintervention group) and comparable months (November–December 2017; preintervention group). Data were analyzed using the 2 test (nominal data) and Mann–Whitney U test (continuous data). Results: Total vancomycin utilization decreased from a monthly average of 114 to 95 DOT/1,000 PD (17% reduction) and from 27 to 14 DOT/1,000 PD for pneumonia (48% reduction). In-patient mortality was unchanged following process implementation at 17.2% versus 17.5% in the pre- and postintervention groups, respectively. Other clinical outcomes were also similar between the pre- and postintervention groups (Table 1). Fewer vancomycin levels were obtained following implementation with 34.4% of patients (0.61 levels per patient) having a level obtained in the preintervention group compared to 21.6% (0.30 levels per patient; P .001) in the postintervention group. Conclusions: Nasopharyngeal MRSA screening of patients prescribed vancomycin for pneumonia is an effective antimicrobial stewardship strategy to reduce unnecessary use of anti-MRSA therapy without negatively impacting clinical outcomes.Funding: NoneDisclosures: None

scholarly journals Short-Term Effect of Orthodontic Treatment with Clear Aligners on Pain and sEMG Activity of Masticatory Muscles

Medicina ◽  
2021 ◽  
Vol 57 (2) ◽  
pp. 178
Author(s):  
Alessandro Nota ◽  
Silvia Caruso ◽  
Shideh Ehsani ◽  
Gianmaria Fabrizio Ferrazzano ◽  
Roberto Gatto ◽  
...  
Keyword(s):  
Surface Electromyography ◽  
Orthodontic Treatment ◽  
Masticatory Muscles ◽  
Rank Test ◽  
Nominal Data ◽  
Chi Square ◽  
Initial Reduction ◽  
Signed Rank Test ◽  
Chi Square Test ◽  
Semg Activity

Background and objectives: The aim of this study is to evaluate mandibular elevator muscles activity and pain on palpation in the early stages of orthodontic treatment with clear aligners using surface electromyography (sEMG). Materials and methods: Surface electromyography (sEMG) activity and pain level on muscle palpation of masseter and anterior temporalis muscles were recorded in a sample of 16 adult subjects (aged 18–32 years; mean 22.5 +/− 3.5 SD) undergoing orthodontic treatment with clear aligners before the treatment (T0), after 1 month of treatment (two clear aligners) (T1), and after 3 months of treatment (T2) (six clear aligners). A chi-square test for nominal data, a Friedman test, and a Wilcoxon-signed rank test as post hoc analysis were applied. Results: No statistically significant differences in muscular pain were observed. At T1, the sEMG activity of masseter muscles at mandibular rest position showed a statistically significant reduction, but after 3 months (T2), the data appeared similar to T0 (p = 0.03 and p = 0.02). Conclusions: During the treatment with clear aligners, subjects could experience an initial reduction in the masseter basal activity after 1 month of treatment. This effect tends to decrease to baseline levels after 3 months of therapy.

Assessment of nominal data requirements for robust estimation of fractional snow cover in alpine-forested terrain

2021 ◽  
Author(s):  
Elzbieta Wisniewski ◽  
Wit Wisniewski
Keyword(s):  
Snow Cover ◽  
Input Data ◽  
Model Performance ◽  
Slope Aspect ◽  
Ann Model ◽  
Linear Modeling ◽  
Nominal Data ◽  
Fractional Snow Cover ◽  
Non Linear ◽  
Input Variables

<p>The presented research examines what minimum combination of input variables are required to obtain state-of-the-art fractional snow cover (FSC) estimates for heterogeneous alpine-forested terrains. Currently, one of the most accurate FSC estimators for alpine regions is based on training an Artificial Neural Network (ANN) that can deconvolve the relationships among numerous compounded and possibly non-linear bio-geophysical relations encountered in alpine terrain. Under the assumption that the ANN optimally extracts available information from its input data, we can exploit the ANN as a tool to assess the contributions toward FSC estimation of each of the data sources, and combinations thereof. By assessing the quality of the modeled FSC estimates versus ground equivalent data, suitable combinations of input variables can be identified. High spatial resolution IKONOS images are used to estimate snow cover for ANN training and validation, and also for error assessment of the ANN FSC results. Input variables are initially chosen representing information already incorporated into leading snow cover estimators (ex. two multispectral bands for NDSI, etc.). Additional variables such as topographic slope, aspect, and shadow distribution are evaluated to observe the ANN as it accounts for illumination incidence and directional reflectance of surfaces affecting the viewed radiance in complex terrain. Snow usually covers vegetation and underlying geology partially, therefore the ANN also has to resolve spectral mixtures of unobscured surfaces surrounded by snow. Multispectral imagery if therefore acquired in the fall prior to the first snow of the season and are included in the ANN analyses for assessing the baseline reflectance values of the environment that later become modified by the snow. In this study, nine representative scenarios of input data are selected to analyze the FSC performance. Numerous selections of input data combinations produced good results attesting to the powerful ability of ANNs to extract information and utilize redundancy. The best ANN FSC model performance was achieved when all 15 pre-selected inputs were used. The need for non-linear modeling to estimate FSC was verified by forcing the ANN to behave linearly. The linear ANN model exhibited profoundly decreased FSC performance, indicating that non-linear processing more optimally estimates FSC in alpine-forested environments.</p>

scholarly journals An Event Group Based Classification Framework for Multi-variate Sequential Data

2017 ◽  
Vol 21 ◽  
Author(s):  
Chao Sun ◽  
David Stirling
Keyword(s):  
Decision Tree ◽  
Classification Problem ◽  
Sequential Data ◽  
Feature Generation ◽  
Data Types ◽  
Nominal Data ◽  
Classification Framework ◽  
Improved Outcomes ◽  
Tree Algorithms ◽  
Industrial Problem

Decision tree algorithms were not traditionally considered for sequential data classification, mostly because feature generation needs to be integrated with the modelling procedure in order to avoid a localisation problem. This paper presents an Event Group Based Classification (EGBC) framework that utilises an X-of-N (XoN) decision tree algorithm to avoid the feature generation issue during the classification on sequential data. In this method, features are generated independently based on the characteristics of the sequential data. Subsequently an XoN decision tree is utilised to select and aggregate useful features from various temporal and other dimensions (as event groups) for optimised classification. This leads the EGBC framework to be adaptive to sequential data of differing dimensions, robust to missing data and accommodating to either numeric or nominal data types. The comparatively improved outcomes from applying this method are demonstrated on two distinct areas – a text based language identification task, as well as a honeybee dance behaviour classification problem. A further motivating industrial problem – hot metal temperature prediction, is further considered with the EGBC framework in order to address significant real-world demands.

Nominal Data Similarity: A Hierarchical Measure

2018 ◽  
Author(s):  
Hao Yu ◽  
Zhaoning Zhang ◽  
Zijie Zhu ◽  
Wang Xiong ◽  
Gen Zhang
Keyword(s):  
Nominal Data ◽  
Data Similarity

scholarly journals National Immunization Program: Computerized System as a tool for new challenges

2015 ◽  
Vol 49 (0) ◽  
Author(s):  
Ana Paula Sayuri Sato
Keyword(s):  
Implementation Process ◽  
Developed Countries ◽  
Nominal Data ◽  
Immunization Program ◽  
High Coverage ◽  
Public And Private ◽  
The Public ◽  
National Immunization ◽  
Innovative Tool ◽  
The Impact

The scope and coverage of the Brazilian Immunization Program can be compared with those in developed countries because it provides a large number of vaccines and has a considerable coverage. The increasing complexity of the program brings challenges regarding its development, high coverage levels, access equality, and safety. The Immunization Information System, with nominal data, is an innovative tool that can more accurately monitor these indicators and allows the evaluation of the impact of new vaccination strategies. The main difficulties for such a system are in its implementation process, training of professionals, mastering its use, its constant maintenance needs and ensuring the information contained remain confidential. Therefore, encouraging the development of this tool should be part of public health policies and should also be involved in the three spheres of government as well as the public and private vaccination services.

scholarly journals Assessment of Knowledge and Attitude Regarding Female and Male Breast Cancer among Adults of South India

2021 ◽  
pp. 13-20
Author(s):  
Anwitha Johns ◽  
Satish Kumar B P ◽  
Lavanya P R
Keyword(s):  
Breast Cancer ◽  
Male Breast Cancer ◽  
Female Breast Cancer ◽  
Incidence Rates ◽  
Male Breast ◽  
Basic Knowledge ◽  
Nominal Data ◽  
Female Breast ◽  
South Indian ◽  
Knowledge And Attitude

Background & Objectives: Breast cancer is the second leading reason for cancer death in women. Incidence rates of male breast cancer have increased by 0.2- 1% per year. The lack of knowledge and awareness of male breast cancer leads to its detection at a late stage in men. This study is to assess the knowledge and attitude of south Indian adults towards male and female breast cancer. Methods: To assess the knowledge and attitude of adults on breast cancer, a questionnaire regarding basic knowledge and attitudes was formulated using Google forms. Numbers and percentages were formed to review categorical and nominal data. Chi-square (χ2) test was used for the comparison between the awareness of female breast cancer and male breast cancer. P < 0.05 was set as the level of significance.

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